High-definition map transformations are essential in autonomous driving systems, enabling interoperability across tools. Ensuring their semantic correctness is challenging, since existing rule-based frameworks rely on manually written formulas and domain-specific functions, limiting scalability. In this paper, We present an LLM-assisted pipeline that jointly generates logical formulas and corresponding executable predicates within a computational FOL framework, extending the map verifier in CommonRoad scenario designer with elevation support. The pipeline leverages prompt-based LLM generation to produce grammar-compliant rules and predicates that integrate directly into the existing system. We implemented a prototype and evaluated it on synthetic bridge and slope scenarios. The results indicate reduced manual engineering effort while preserving correctness, demonstrating the feasibility of a scalable, semi-automated human-in-the-loop approach to map-transformation verification.

翻译：高精地图变换在自动驾驶系统中至关重要，可实现跨工具互操作性。确保其语义正确性具有挑战性，因为现有的基于规则的框架依赖手动编写的公式和领域特定函数，限制了可扩展性。本文提出一种LLM辅助流程，在计算性一阶逻辑框架内联合生成逻辑公式及对应的可执行谓词，扩展了CommonRoad场景设计器中支持高程的地图验证器。该流程利用基于提示的LLM生成技术，产出符合语法规范的规则和谓词，并直接集成到现有系统中。我们实现了原型系统，并在合成的桥梁与斜坡场景中进行评估。结果表明，该方法在保持正确性的同时减少了人工工程负担，证明了可扩展、半自动化的人机协同地图变换验证方法的可行性。